Each year, the federal government makes more than $3 trillion in payments, most of which are proper. However, since 2002, agencies estimate they have made more than $1.2 trillion ($144 billion in FY 2016 alone) in improper payments due to error or fraud. By comparison, the entire federal government’s FY 2016 R&D budget was $145 billion — almost the same as that year’s improper payments estimate. These needless expenditures restrict spending options, add to the federal debt, enrich criminals, siphon off funds needed to serve the interests of the American public, and undermine public confidence in government.

Data analytics can prevent and identify fraud and other improper payments through an array of tools and data sets. Federal agencies such as the Centers for Medicare and Medicaid Services’ (CMS) Fraud Prevention System and the Department of Labor’s Unemployment Insurance Integrity Center of Excellence have used analytic efforts to combat improper payments. In recent years, Congress focused attention on data analytics in the Improper Payments Elimination and Recovery Improvement Act of 2012 (P.L. 112-248) with the establishment of the Do Not Pay Initiative, and in the Fraud Reduction and Data Analytics Act of 2015 (P.L. 114-186) with the requirement for a federal interagency library of data analytics and data sets. However, agencies all too often use data analytics to identify problems that have already occurred and to facilitate improper payment reporting.

Further, agencies frequently work in isolation to address their improper payments problems. They often lack readily available frameworks to facilitate ongoing communication and information sharing to combat common or systemic improper payment problems caused by such things as the use of fraudulent identities or the failure to provide valid, timely eligibility information. To complicate matters, legislative mandates or prohibitions sometimes produce unintended consequences that create the potential for improper payments or make it more difficult to prevent or detect them. For example, complicated eligibility rules can increase the potential for errors and can create “loopholes” that make it easier to commit fraud. In the end, agencies too often find themselves making payments, identifying improper ones afterwards, and then trying to recoup the funds — the fundamentally flawed model called “pay & chase.”

In February 2016, The MITRE Corporation, a not-for-profit company that operates federally funded research and development centers, presented the results of an independent, internally funded study of government-wide payment integrity. MITRE recommended a more comprehensive approach to address the problem. The concept would include:

  • Public-private partnerships and cross-agency work groups, forming networks of partners to collaborate on solving systemic improper payments problems.
  • Information Sharing and Analysis Centers — ISACs — like those used to identify and fight cyber-crime, and small analytic cell approaches such as those used in law enforcement and counter-terrorism.
  • An increased focus on predictive analytics — an approach to data analysis that relies on “learning systems.” 
  • Mechanisms and methodologies to address data access and sharing issues.
  • A communications mechanism similar to the near-real-time efforts of joint task forces and ISACs to provide actionable predictive information.

An example of this approach is the Healthcare Fraud Prevention Partnership (HFPP), a public-private partnership sponsored by CMS with nearly 80 members from states, law enforcement organizations, and health insurance groups. The HFPP focuses on performing predictive analysis in the form of studies, providing the ability to proactively introduce flags, edit checks and process improvements to prevent improper payments and identify potential fraud. The partnership also provides a platform for members to quickly share information about potential fraud schemes they have identified and to benefit from analytic studies. Since its inception, the HFPP has identified opportunities to recover many millions of dollars of potential fraud.

Public-private partnerships — supported by mechanisms for information sharing, joint analysis, and powerful predictive analytics — can greatly enhance agencies’ capabilities to address systemic improper payments problems. Agencies would be better positioned to identify the indicators, the locations, the conditions, and more, that lead to improper payments and to proactively address them to prevent these payments. While some agencies are engaged in extensive data analytics, more needs to be done to create the partnerships and capabilities necessary to use data effectively across government to deter fraud and prevent other improper payments.

Gordon C. Milbourn III leads cross-government efforts to address improper payments and financial fraud issues for The MITRE Corporation’s government sponsors.

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